线型回归模型LinearRegression

class sklearn.linear_model.LinearRegression(fit_intercept=True, normalize=False, copy_X=True, n_jobs=None)
Parameters:
fit_intercept : boolean, optional, default True
whether to calculate the intercept for this model. If set to False, no intercept will be used in calculations (e.g. data is expected to be already centered).
模型中时候加入截距，默认加入
normalize : boolean, optional, default False
This parameter is ignored when fit_intercept is set to False. If True, the regressors X will be normalized before regression by subtracting the mean and dividing by the l2-norm.
是否对数据进行归一化，默认不进行
copy_X : boolean, optional, default True
If True, X will be copied; else, it may be overwritten.
n_jobs : int or None, optional (default=None)
The number of jobs to use for the computation. This will only provide speedup for n_targets > 1 and sufficient large problems. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.
是否加速，默认不加速。

examples:

import numpy as np
>>> from sklearn.linear_model import LinearRegression
>>> X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])
>>> # y = 1 * x_0 + 2 * x_1 + 3
>>> y = np.dot(X, np.array([1, 2])) + 3
>>> reg = LinearRegression().fit(X, y)
>>> reg.score(X, y)
1.0
>>> reg.coef_#获取斜率
array([1., 2.])
>>> reg.intercept_ #获取截距
3.0000...
>>> reg.predict(np.array([[3, 5]]))
array([16.])